Journal of Computer Science and Technology

, Volume 29, Issue 6, pp 1083–1093

CPL: Detecting Protein Complexes by Propagating Labels on Protein-Protein Interaction Network

  • Qi-Guo Dai
  • Mao-Zu Guo
  • Xiao-Yan Liu
  • Zhi-Xia Teng
  • Chun-Yu Wang
Regular Paper

DOI: 10.1007/s11390-014-1492-z

Cite this article as:
Dai, QG., Guo, MZ., Liu, XY. et al. J. Comput. Sci. Technol. (2014) 29: 1083. doi:10.1007/s11390-014-1492-z
  • 55 Downloads

Abstract

Proteins usually bind together to form complexes, which play an important role in cellular activities. Many graph clustering methods have been proposed to identify protein complexes by finding dense regions in protein-protein interaction networks. We present a novel framework (CPL) that detects protein complexes by propagating labels through interactions in a network, in which labels denote complex identifiers. With proper propagation in CPL, proteins in the same complex will be assigned with the same labels. CPL does not make any strong assumptions about the topological structures of the complexes, as in previous methods. The CPL algorithm is tested on several publicly available yeast protein-protein interaction networks and compared with several state-of-the-art methods. The results suggest that CPL performs better than the existing methods. An analysis of the functional homogeneity based on a gene ontology analysis shows that the detected complexes of CPL are highly biologically relevant.

Keywords

protein complex detection label propagation protein-protein interaction graph clustering bioinformatics 

Supplementary material

11390_2014_1492_MOESM1_ESM.pdf (91 kb)
ESM 1(PDF 91 kb)

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Qi-Guo Dai
    • 1
  • Mao-Zu Guo
    • 1
  • Xiao-Yan Liu
    • 1
  • Zhi-Xia Teng
    • 1
    • 2
  • Chun-Yu Wang
    • 1
  1. 1.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina
  2. 2.School of Information and Computer EngineeringNortheast Forestry UniversityHarbinChina

Personalised recommendations